import geopandas
import libpysalVisualization for Area Unit Data
Spring 2023
Statistical Visualization of Area Unit Data
Geovisualization
Choropleths
Areal Unit Data
south = libpysal.examples.load_example('South')libpysal.examples.explain('South')south_gdf = geopandas.read_file(south.get_path('south.shp'))south_gdf.plot()<AxesSubplot: >
import seabornseaborn.displot(south_gdf, x='HR60')<seaborn.axisgrid.FacetGrid at 0x7f3e1cbac370>
south_gdf.explore(column='HR60')Make this Notebook Trusted to load map: File -> Trust Notebook
south_gdf.HR60.describe()count 1412.000000
mean 7.292144
std 6.421018
min 0.000000
25% 3.213471
50% 6.245125
75% 9.956272
max 92.936803
Name: HR60, dtype: float64south_gdf.plot(column='HR60')<AxesSubplot: >
south_gdf.plot(column='HR60', scheme='Quantiles')<AxesSubplot: >
south_gdf.plot(column='HR60', scheme='Quantiles', legend=True)<AxesSubplot: >
Classification Schemes
\[c_j \lt y_i \le c_{j+1} \forall y_i \in C_j\]
where \(y_i\) is the value for the attribute at location \(i\), \(j\) is a class index, and \(c_j\) represents the lower bound of interval \(j\).
import mapclassifymapclassify.Quantiles(south_gdf.HR60)Quantiles
Interval Count
----------------------
[ 0.00, 2.50] | 283
( 2.50, 5.10] | 282
( 5.10, 7.62] | 282
( 7.62, 10.98] | 282
(10.98, 92.94] | 283mapclassify.Quantiles(south_gdf.HR60, k=10)Quantiles
Interval Count
----------------------
[ 0.00, 0.00] | 180
( 0.00, 2.50] | 103
( 2.50, 3.93] | 141
( 3.93, 5.10] | 141
( 5.10, 6.25] | 141
( 6.25, 7.62] | 141
( 7.62, 9.19] | 141
( 9.19, 10.98] | 141
(10.98, 14.31] | 141
(14.31, 92.94] | 142mapclassify.EqualInterval(south_gdf.HR60, k=10)EqualInterval
Interval Count
----------------------
[ 0.00, 9.29] | 1000
( 9.29, 18.59] | 358
(18.59, 27.88] | 39
(27.88, 37.17] | 8
(37.17, 46.47] | 4
(46.47, 55.76] | 2
(55.76, 65.06] | 0
(65.06, 74.35] | 0
(74.35, 83.64] | 0
(83.64, 92.94] | 1mapclassify.MaximumBreaks(south_gdf.HR60, k=10)MaximumBreaks
Interval Count
----------------------
[ 0.00, 29.42] | 1400
(29.42, 30.74] | 1
(30.74, 33.40] | 1
(33.40, 35.94] | 1
(35.94, 39.00] | 4
(39.00, 43.29] | 1
(43.29, 48.96] | 1
(48.96, 52.69] | 1
(52.69, 73.12] | 1
(73.12, 92.94] | 1mapclassify.FisherJenks(south_gdf.HR60, k=10)FisherJenks
Interval Count
----------------------
[ 0.00, 1.71] | 216
( 1.71, 4.45] | 278
( 4.45, 7.08] | 287
( 7.08, 10.02] | 288
(10.02, 13.59] | 176
(13.59, 19.60] | 121
(19.60, 28.77] | 34
(28.77, 40.74] | 8
(40.74, 53.30] | 3
(53.30, 92.94] | 1mapclassify.BoxPlot(south_gdf.HR60)BoxPlot
Interval Count
----------------------
( -inf, -6.90] | 0
(-6.90, 3.21] | 353
( 3.21, 6.25] | 353
( 6.25, 9.96] | 353
( 9.96, 20.07] | 311
(20.07, 92.94] | 42mapclassify.HeadTailBreaks(south_gdf.HR60)HeadTailBreaks
Interval Count
----------------------
[ 0.00, 7.29] | 802
( 7.29, 12.41] | 405
(12.41, 18.18] | 147
(18.18, 26.87] | 40
(26.87, 38.73] | 13
(38.73, 56.98] | 4
(56.98, 92.94] | 1Map Customization
- Legends
- Color Schemes
Legends
south_gdf[['STATE_NAME', 'HR60', 'HR90']].head()| STATE_NAME | HR60 | HR90 | |
|---|---|---|---|
| 0 | West Virginia | 1.682864 | 0.946083 |
| 1 | West Virginia | 4.607233 | 1.234934 |
| 2 | West Virginia | 0.974132 | 2.621009 |
| 3 | West Virginia | 0.876248 | 4.461577 |
| 4 | Delaware | 4.228385 | 6.712736 |
south_gdf['increased' ] = south_gdf.HR90 > south_gdf.HR60south_gdf.plot(column='increased', categorical=True, legend=True);
v = south_gdf.increased.map({True: 'Increased', False: 'Decreased'})south_gdf['Increased'] = vsouth_gdf.plot(column='Increased', categorical=True, legend=True);
south_gdf.plot(column='Increased', categorical=True, legend=True,
legend_kwds={'bbox_to_anchor': (1.3, 1)});
south_gdf.plot(column='Increased', categorical=True, legend=True,
legend_kwds={'bbox_to_anchor': (1.3, 1),
'title':'Homicide Rates 1960-1990'},
);
south_gdf.plot(column='Increased', categorical=True, legend=True,
legend_kwds={'bbox_to_anchor': (0, 1),
'title':'Homicide Rates 1960-1990'},
);
south_gdf.plot(column='Increased', categorical=True, legend=True,
legend_kwds={'bbox_to_anchor': (-0.1, 1),
'title':'Homicide Rates 1960-1990'},
);
Color schemes
For more info see matplotlib
Sequential Color Schemes
south_gdf.plot(column='HR60', scheme='Quantiles', legend=True,
legend_kwds={'bbox_to_anchor': (1.3, 1)},
cmap='Blues');
south_gdf.plot(column='HR60', scheme='Quantiles', legend=True,
legend_kwds={'bbox_to_anchor': (1.3, 1)},
cmap='Greens');
south_gdf.plot(column='HR60', scheme='Quantiles', legend=True,
legend_kwds={'bbox_to_anchor': (1.3, 1)},
cmap='YlGnBu');
Diverging Color Schme
south_gdf.plot(column='Increased', categorical=True, legend=True,
legend_kwds={'bbox_to_anchor': (-0.1, 1),
'title':'Homicide Rates 1960-1990'},
cmap='coolwarm',
);
south_gdf.plot(column='Increased', categorical=True, legend=True,
legend_kwds={'bbox_to_anchor': (-0.1, 1),
'title':'Homicide Rates 1960-1990'},
cmap='bwr',
);
Qualitative Color Scheme
south_gdf.plot(column='STATE_NAME', categorical=True)<AxesSubplot: >
south_gdf.plot(column='STATE_NAME', categorical=True, legend=True)<AxesSubplot: >
south_gdf.plot(column='STATE_NAME', categorical=True, legend=True,
legend_kwds={'bbox_to_anchor': (0, 1)})<AxesSubplot: >
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1])
ax.axis('off')
south_gdf.plot(column='STATE_NAME', categorical=True, legend=True,
legend_kwds={'bbox_to_anchor': (0, 1)}, ax=ax);
Comparisons (Sequential)
south_gdf.plot(column='HR60', scheme='Quantiles', legend=True,
legend_kwds={'bbox_to_anchor': (1.3, 1)},
cmap='YlGnBu', k=10);
south_gdf.plot(column='HR60', scheme='MaximumBreaks', legend=True,
legend_kwds={'bbox_to_anchor': (1.3, 1)},
cmap='YlGnBu', k=10);
south_gdf.plot(column='HR60', scheme='FisherJenks', legend=True,
legend_kwds={'bbox_to_anchor': (1.3, 1)},
cmap='YlGnBu', k=10);